1. A. Opelt, M. Fussenegger, A. Pinz, P. Auer
Weak Hypotheses
and Boosting
for Generic Object Detection
and
Recognition
Andreas Opelt (Graz University of Technology and University of Leoben)

2. Andreas Opelt (Graz University of Technology and University of Leoben)
Agenda
The Basic Idea
Our Framework for generic Object Recognition 
The techniques used
The Learning Model 
Our Model
The Weak Hypotheses Finder
Experiments
Discussion / Outlook
Andreas Opelt (Graz University of Technology and University of Leoben)

3. We want to go towards ‘real’ Generic Object Recognition!
The Basic Idea 1/2
Problems ?
Object is located anywhere in the image!
No pre-selection of the object !
Any instance of the object category!
Not special images for learning!
We want to go towards ‘real’ Generic Object Recognition!
Arbitrary view of the object!
Any background clutter!
Objects shown in any arbitrary scale!
Not only for a special category of objects!
Andreas Opelt (Graz University of Technology and University of Leoben)

4. The Basic Idea 2/2
We want to go towards ‘real’ Generic Object Recognition!
We want to go towards ‘real’ Generic Object Recognition!
Graz database; Bikes, Persons, Background
Oxford database; (Fergus, Perona and Zisserman, CVPR 2003)
Agarwal and Roth, ECCV 2002,
Cars side database
Andreas Opelt (Graz University of Technology and University of Leoben)

5. Andreas Opelt (Graz University of Technology and University of Leoben)
The Framework
Andreas Opelt (Graz University of Technology and University of Leoben)

6. Andreas Opelt (Graz University of Technology and University of Leoben)
Region Extraction 1/2
[Mikolajczyk/Schmid 2001]
Data reduction: Threshold
[Mikolajczyk/Schmid 2001]
Data reduction: Threshold
[Lowe 1999] (Diff. of Gaussian)
Data reduction: Clustering
Andreas Opelt (Graz University of Technology and University of Leoben)

7. Andreas Opelt (Graz University of Technology and University of Leoben)
Region Extraction 2/2
Andreas Opelt (Graz University of Technology and University of Leoben)

8. Andreas Opelt (Graz University of Technology and University of Leoben)
Region Normalization
Homomorphic Filtering [Gonzales and Woods, C. 4.5.]
Size Normalization
Andreas Opelt (Graz University of Technology and University of Leoben)

9. Andreas Opelt (Graz University of Technology and University of Leoben)
The Framework
Andreas Opelt (Graz University of Technology and University of Leoben)

10. Andreas Opelt (Graz University of Technology and University of Leoben)
Local Descriptors
Subsampled Grayvalues
Basic Moments (Dim=10)
[L. Van Gool 1996]
Dim=9
[D. Lowe 1999]
Dim=128 (3 orient. planes, 8x8px)
Andreas Opelt (Graz University of Technology and University of Leoben)

11. Andreas Opelt (Graz University of Technology and University of Leoben)
The Framework
Andreas Opelt (Graz University of Technology and University of Leoben)

12. Andreas Opelt (Graz University of Technology and University of Leoben)
The Learning Model 1/3
Input:
Weak Hypotheses:
 Threshold, Weight
Output:
Andreas Opelt (Graz University of Technology and University of Leoben)

13. Andreas Opelt (Graz University of Technology and University of Leoben)
The Learning Model 1/3
Select best Weak Hypothesis
Calculate Threshold
Andreas Opelt (Graz University of Technology and University of Leoben)

14. Andreas Opelt (Graz University of Technology and University of Leoben)
The Learning Model 3/3
Andreas Opelt (Graz University of Technology and University of Leoben)

15. Andreas Opelt (Graz University of Technology and University of Leoben)
Experiments 1/6
Category: Bikes  some Weak Hypotheses
Andreas Opelt (Graz University of Technology and University of Leoben)

16. Andreas Opelt (Graz University of Technology and University of Leoben)
Experiments 2/6
 Testing
BIKE !
Andreas Opelt (Graz University of Technology and University of Leoben)

17. Andreas Opelt (Graz University of Technology and University of Leoben)
Experiments 3/6
 Testing
BIKE !
BIKE !
Andreas Opelt (Graz University of Technology and University of Leoben)

18. Andreas Opelt (Graz University of Technology and University of Leoben)
Experiments 4/6
 Testing
NO BIKE !
NO BIKE !
Andreas Opelt (Graz University of Technology and University of Leoben)

19. Andreas Opelt (Graz University of Technology and University of Leoben)
Experiments 5/6
 Testing
NO BIKE !
Andreas Opelt (Graz University of Technology and University of Leoben)

20. Andreas Opelt (Graz University of Technology and University of Leoben)
Experiments 6/6
Facts:
Andreas Opelt (Graz University of Technology and University of Leoben)

21. Andreas Opelt (Graz University of Technology and University of Leoben)
Discussion / Outlook
Further Experimental Evaluation
Multiclass Categorisation
Combination with other Types of Regions
Andreas Opelt (Graz University of Technology and University of Leoben)

22. Andreas Opelt (Graz University of Technology and University of Leoben)
Conclusion
Generic object recognition
A new Framework
A new Learning Model
Good Results
Andreas Opelt (Graz University of Technology and University of Leoben)

23. Andreas Opelt (Graz University of Technology and University of Leoben)
Thank you !
Generic object recognition; not an easy task!
Thanks to the Lava Project and the FWF Project – FSP Cognitive Vision
Andreas Opelt (Graz University of Technology and University of Leoben)